enrich

name	enrich
description	Enrich people in a knowledge graph or wiki with contact and social media information — LinkedIn, email, phone, Twitter/X — using premium enrichment APIs via the agentcash CLI. Use this skill when the user wants to enrich contacts, find someone's LinkedIn or email, fill in missing contact info for people in their wiki or knowledge base, or says 'enrich', 'find contact info', 'look up LinkedIn', 'fill in missing info', or anything about augmenting people/contact pages with external data.
argument-hint	[all | <person-name>]

Enrich Knowledge Graph

Enrich people pages in a user's knowledge graph or wiki with contact and social media data from premium APIs (Minerva, Apollo) via agentcash. Minerva is the primary API — it returns richer data (work/personal emails, phones, Twitter, demographics, wealth signals) at the same price as Apollo ($0.05/person). Apollo is the fallback for name+company lookups when no LinkedIn URL is available. The APIs cost real money (typically 5-7 cents per person), so the skill guides the user through funding before making any paid calls.

MCP vs CLI

If the agentcash MCP server is available (i.e. you have access to tools like mcp__agentcash__fetch), always use the MCP tools instead of the CLI commands. They are faster, don't require npx, and work directly within the conversation. Here is the mapping:

CLI command	MCP tool
npx agentcash balance	mcp__agentcash__get_balance
npx agentcash fetch <url> ...	mcp__agentcash__fetch (pass url, method, body)
npx agentcash check <url>	mcp__agentcash__check_endpoint_schema (pass url)
npx agentcash discover <origin>	mcp__agentcash__discover_api_endpoints (pass url)
npx agentcash accounts	mcp__agentcash__list_accounts
npx agentcash onboard <code>	mcp__agentcash__redeem_invite (pass the code)

The CLI commands shown throughout this skill are the fallback for when the MCP server is not installed. If MCP tools are available, use them everywhere you see a CLI example below.

Interaction rules

Only ever ask the user one question at a time. Never present multiple decisions or questions simultaneously. Be conversational — guide them step by step.

Workflow

Step 0: Discover the knowledge graph

Before doing anything else, understand the structure of the user's knowledge graph. Don't assume any particular directory layout, frontmatter schema, or file naming convention.

1. Look at the project's CLAUDE.md, README, or any top-level documentation to understand the project structure.
2. Find where people pages live. Common patterns:
   • wiki/people/, people/, contacts/, network/
   • Markdown files with tags: [person] or type: person in frontmatter
   • A directory index or manifest that lists people
3. Read 2-3 sample people pages to learn the frontmatter schema. Look for fields that store:
   • Name: title, name, first_name/last_name, full_name, etc.
   • Contact info: email, linkedin, twitter, x, phone, social, etc.
   • Context: org, company, role, title, location, etc.
4. Note the schema you discover — you'll need it when writing enrichment data back.

If you can't find people pages, ask the user: "Where do your people pages live?"

Step 1: Check balance

Run:

npx agentcash balance

If the balance is zero or very low (under $0.50):

1. Explain: "Enrichment uses premium APIs. It typically costs 5 cents per person with Minerva Enrich (primary) or Apollo (fallback). To enrich 20 people you'd need about $1."
2. Ask: "Would you like to sign up for free credits, or add funds directly to your wallet?"
3. If they want free credits:
   • Run open https://agentcash.dev/onboard to open signup in a browser.
   • Tell the user: "Complete the signup flow — it will give you a code. Paste it here and I'll activate your credits."
   • When they paste the code, run npx agentcash onboard <code> to redeem it. This is what actually deposits the credits into their wallet.
4. If they want to add funds: run npx agentcash accounts and give them the deposit URL from the output so they can send USDC.

After the user confirms they have funds, re-check balance with npx agentcash balance and proceed.

Step 2: Scan knowledge graph

1. List all people pages from the directory discovered in Step 0.
2. Read each person page and parse whatever frontmatter format the project uses.
3. Extract what you can identify as: name, email, linkedin, phone, twitter/x, org, role, location.
4. Classify each person:
   • Complete: has at least email AND linkedin
   • Partial: has some contact info but missing email or linkedin
   • Missing: has no email, no linkedin, and no phone
5. Present a one-line summary: "Found N people. M are missing contact information."
6. Ask: "Want to enrich all M people with missing info, or pick specific ones?"

If the user passed an argument (e.g., /enrich-knowledge-graph John Doe), skip the scan summary and go straight to enriching that person.

Step 3: Enrich each person

For each person to enrich, build the best possible query from their existing data and call the APIs.

Strategy: use what you have

The more identifying info you provide, the better the match. Prioritize fields in this order:

• LinkedIn URL (strongest signal — use Minerva Enrich directly)
• Email or phone (use Minerva Resolve to find LinkedIn, then Enrich)
• First name + last name + company/org (use Apollo as fallback)
• First name + last name alone (weakest — try Minerva Resolve with email/phone if available, otherwise Apollo)

Primary path: Minerva Enrich (~$0.05)

Minerva returns richer data than Apollo at the same price: work emails, personal emails, phone numbers, Twitter, demographics, wealth signals, and full work history. It works best when you have a LinkedIn URL.

npx agentcash fetch https://stableenrich.dev/api/minerva/enrich \
  --method POST \
  --body '{"records":[{"record_id":"1","linkedin_url":"https://linkedin.com/in/johndoe"}]}'

You can also pass minerva_pid, first_name/last_name, emails, or phones in the record. LinkedIn URL gives the best results.

From the response, extract:

• professional_emails[0].email_address -> work email
• personal_emails[0].email_address -> personal email
• phones[0].phone_number -> phone
• twitter_url -> twitter/x
• linkedin_url -> linkedin
• work_experience (where work_status: "current") -> role, org
• education_experience -> education
• estimated_income_range, estimated_wealth_range -> financial signals (optional, don't write to wiki unless schema supports it)

Finding LinkedIn first: Minerva Resolve (~$0.02)

If you don't have a LinkedIn URL but have an email or phone, use Minerva Resolve to find one:

npx agentcash fetch https://stableenrich.dev/api/minerva/resolve \
  --method POST \
  --body '{"records":[{"record_id":"1","first_name":"John","last_name":"Doe","emails":["[e-mail]"]}]}'

Important: Minerva Resolve requires at least one email or phone number. It cannot match on name alone. If is_match: true, follow up with Minerva Enrich using the returned linkedin_url or minerva_pid.

Fallback: Apollo People Enrich (~$0.05)

Use Apollo when Minerva fails or when you only have a name + company (no LinkedIn URL, no email, no phone). Apollo can match on name + organization without needing a LinkedIn URL.

npx agentcash fetch https://stableenrich.dev/api/apollo/people-enrich \
  --method POST \
  --body '{"first_name":"John","last_name":"Doe","organization_name":"Acme Corp"}'

Include whichever fields are available: first_name, last_name, email, linkedin_url, organization_name, domain. Apollo returns: email, LinkedIn URL, phone numbers, Twitter handle, title, company, location.

Validate matches

After each enrichment call, check whether the result looks like a genuine match — does the name, company, and role align with what you already know about this person? If the match looks wrong, skip it and note it in the report. Don't write bad data to the knowledge graph.

Step 4: Update pages

For each successfully enriched person:

1. Re-read the current page (it may have changed).
2. Update only the frontmatter or structured metadata — do not touch the article body.
3. Only fill in missing fields. Never overwrite existing data. If the page already has an email and Apollo returns a different one, leave the existing one.
4. Write the enrichment data back using the same field names and format the project already uses (discovered in Step 0). Don't introduce new field names that are inconsistent with the existing schema.
5. Common mappings from API responses (adapt field names to match the project's schema):
   • Minerva Enrich:
     • professional_emails[0].email_address -> email field (prefer rank 1)
     • personal_emails[0].email_address -> personal email (if schema supports it)
     • linkedin_url -> linkedin field
     • phones[0].phone_number -> phone field
     • twitter_url -> twitter/x field
     • work_experience (first entry where work_status: "current") -> role (work_title), org (work_company_name)
     • Work experience work_city + work_state -> location field
   • Apollo People Enrich (fallback):
     • email -> email field
     • linkedin_url -> linkedin field
     • phone_numbers[0].sanitized_number -> phone field
     • twitter_url -> twitter/x field (extract handle from URL if the project stores handles)
     • title -> role/title field
     • organization.name -> org/company field
     • city + state -> location field
6. If the project uses an updated date field, set it to today.

Step 5: Report

After all enrichments, show a summary:

• Total people processed
• Successfully enriched (with breakdown: emails found, LinkedIn profiles found, phone numbers found)
• Failed to enrich (list names)
• Estimated total cost (count API calls: Minerva Enrich = $0.05 each, Minerva Resolve = $0.02, Apollo Enrich = $0.05)

API reference

If you're unsure about the exact request/response schema for any endpoint, run:

npx agentcash check <url>

To see all available enrichment endpoints:

npx agentcash discover stableenrich.dev